Abstract
Classifying large quantities of multidimensional data (e.g. remotely sensed agricultural data)[1] requires efficient and effective classification techniques and the construction of certain transformations of a dimension-reducing, information-preserving nature. This paper will deal with the construction of transformations that minimally degrade information (i.e. class separability). We will only consider the construction of linear dimension-reducing transformations for multivariate normal populations and information content will be measured by divergence[2].
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